Adaptive response priors in context-dependent decision-making

Olga Lositsky, Princeton University, Princeton, NJ, USA

Michael Shvartsman, Princeton University

Robert C. Wilson, University of Arizona

Jonathan D. Cohen, Princeton University

Abstract

Context (such as our location or current goal) informs everyday
decisions, both by predicting stimuli and determining relevant responses. How do
we develop priors that are general enough to apply in various contexts yet
specific enough to maximize reward in a given context? We investigated this using
the AX-CPT, a task in which a cue determines which button to press for a probe
that appears seconds later. We manipulated the frequency of the probe given the
cue across participants and built a diffusion model to estimate how the cue
informs participants’ priors for the decision. We found that
participants’ context-dependent priors were closer to each other and less
extreme than those predicted by a model that maximizes reward rate given the true
stimulus frequencies. However, participants’ priors were optimal given
their subjective frequency estimates, which showed that they averaged response
probabilities across cues when the cues made sufficiently similar predictions.